This page is located in archive.


Datum Č.T. S/L Náplň Učitel Materiály
25-26.09.2023 1 L Intro: Introduction to the course, Python, NumPy, GitHub, Jupyter Notebook and the environment. PV Python & Numpy
Notebook Notebook (Zip)
02-03.10.2023 2 S 1D regression and 2D classification: Revision of the regression and classification theory, analytic gradient computation, gradient in computational graph and loss minimization. KZ template_for_students.zip
09-10.10.2023 3 L Autograd: Computational graphs, backpropagation and the automatic gradient computation. JV HW1: Autograd
engine_lab engine_ref
16-17.10.2023 4 S Classification I - Intro: Revision of the classification theory, KNN and the linear classifier. AK
23-24.10.2023 5 L Classification II - NN: Classification using neural networks. AK HW2: Classification
30-31.10.2023 6 S CNN I - Intro: Introduction to the convolution and to the PyTorch library. JV lab06.zip lab06_ref.zip
06-07.11.2023 7 L Optimization: Convergence rate, oscillations, diminishing gradients. KZ optimizers_student_template.py.zip
13-14.11.2023 8 S CNN II: Training a classifier: Training a simple CNN classifier. JV lab08.zip lab08_ref.zip
20-21.11.2023 9 L Dean's day
27-28.11.2023 10 S CNN III - Semantic segmentation: Segmentation of images using CNN. PV HW 3 - Segmentation HW3
04-05.12.2023 11 L RL I Intro:Introduction to the reinforcement learning. Policy gradient. TT HW4: Reinforcement learning
11-12.12.2023 12 S RL II - Deep learning:Deep reinforcement learning. TT
18-19.12.2023 13 L CNN IV - Semantic Segmentation: Presentations, Team projects PV
08-09.01.2023 14 S Transformers: Applications of the transformers. DC lab05.zip
courses/b3b33urob/tutorials/start.txt · Last modified: 2024/01/08 16:01 by capekda4